def driving_process(params, step, sL, s):
#placeholder plumbing for random processes
arrival_rate = 10 / (1 + s['sentiment'])
rv1 = np.random.rand()
new_participant = bool(rv1 < 1 / arrival_rate)
supporters = get_edges_by_type(s['network'], 'support')
len_parts = len(get_nodes_by_type(s['network'], 'participant'))
supply = s['supply']
expected_holdings = .1 * supply / len_parts
if new_participant:
h_rv = expon.rvs(loc=0.0, scale=expected_holdings)
new_participant_holdings = h_rv
else:
new_participant_holdings = 0
network = s['network']
affinities = [network.edges[e]['affinity'] for e in supporters]
median_affinity = np.median(affinities)
proposals = get_nodes_by_type(network, 'proposal')
fund_requests = [
network.nodes[j]['funds_requested'] for j in proposals
if network.nodes[j]['status'] == 'candidate'
]
funds = s['funds']
total_funds_requested = np.sum(fund_requests)
proposal_rate = 1 / median_affinity * (1 + total_funds_requested / funds)
rv2 = np.random.rand()
new_proposal = bool(rv2 < 1 / proposal_rate)
sentiment = s['sentiment']
funds = s['funds']
scale_factor = funds * sentiment**2 / 10000
if scale_factor < 1:
scale_factor = 1
#this shouldn't happen but expon is throwing domain errors
if sentiment > .4:
funds_arrival = expon.rvs(loc=0, scale=scale_factor)
else:
funds_arrival = 0
return ({
'new_participant': new_participant,
'new_participant_holdings': new_participant_holdings,
'new_proposal': new_proposal,
'funds_arrival': funds_arrival
})
def complete_proposal(params, step, sL, s, _input):
network = s['network']
participants = get_nodes_by_type(network, 'participant')
proposals = get_nodes_by_type(network, 'proposal')
competitors = get_edges_by_type(network, 'conflict')
completed = _input['completed']
for j in completed:
network.nodes[j]['status'] = 'completed'
for c in proposals:
if (j, c) in competitors:
conflict = network.edges[(j, c)]['conflict']
for i in participants:
network.edges[(i, c)]['affinity'] = network.edges[
(i, c)]['affinity'] * (1 - conflict)
for i in participants:
force = network.edges[(i, j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment,
force,
decay=0)
failed = _input['failed']
for j in failed:
network.nodes[j]['status'] = 'failed'
for i in participants:
force = -network.edges[(i, j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment,
force,
decay=0)
key = 'network'
value = network
return (key, value)